Tutorial `DataSaver`#

Note

Because this documentation consists of static html, the live_plot and live_info widget is not live. Download the notebook in order to see the real behaviour. [1]

If the function that you want to learn returns a value along with some metadata, you can wrap your learner in an adaptive.DataSaver.

In the following example the function to be learned returns its result and the execution time in a dictionary:

from operator import itemgetter


def f_dict(x):
    """The function evaluation takes roughly the time we `sleep`."""
    import random
    from time import sleep

    waiting_time = random.random()
    sleep(waiting_time)
    a = 0.01
    y = x + a**2 / (a**2 + x**2)
    return {"y": y, "waiting_time": waiting_time}


# Create the learner with the function that returns a 'dict'
# This learner cannot be run directly, as Learner1D does not know what to do with the 'dict'
_learner = adaptive.Learner1D(f_dict, bounds=(-1, 1))

# Wrapping the learner with 'adaptive.DataSaver' and tell it which key it needs to learn
learner = adaptive.DataSaver(_learner, arg_picker=itemgetter("y"))

learner.learner is the original learner, so learner.learner.loss() will call the correct loss method.

runner = adaptive.Runner(learner, loss_goal=0.1)

runner.live_info()

runner.live_plot(plotter=lambda lrn: lrn.learner.plot(), update_interval=0.1)

Now the DataSavingLearner will have an dictionary attribute extra_data that has x as key and the data that was returned by learner.function as values.

learner.extra_data

OrderedDict([(-1.0,
              {'y': -0.9999000099990001, 'waiting_time': 0.05240389211136409}),
             (1.0, {'y': 1.000099990001, 'waiting_time': 0.8587050776596159}),
             (0.0, {'y': 1.0, 'waiting_time': 0.9636861061467198}),
             (-0.75,
              {'y': -0.7498222538215429, 'waiting_time': 0.06683442246412086}),
             (-0.5,
              {'y': -0.4996001599360256, 'waiting_time': 0.26916221870008583}),
             (0.5,
              {'y': 0.5003998400639744, 'waiting_time': 0.5455846073829308}),
             (-0.25,
              {'y': -0.24840255591054314, 'waiting_time': 0.6238034489998049}),
             (-0.375,
              {'y': -0.3742893942085628, 'waiting_time': 0.22905691823101682}),
             (-0.125,
              {'y': -0.11864069952305246, 'waiting_time': 0.6532680389055059}),
             (-0.0625,
              {'y': -0.0375390015600624, 'waiting_time': 0.10809245726483052}),
             (0.75,
              {'y': 0.7501777461784571, 'waiting_time': 0.7344416244013923}),
             (-0.03125,
              {'y': 0.06163824383164006, 'waiting_time': 0.8934815929748705}),
             (0.25,
              {'y': 0.2515974440894569, 'waiting_time': 0.9570626791536889}),
             (-0.015625,
              {'y': 0.27495388762769585, 'waiting_time': 0.6373813653543834}),
             (0.125,
              {'y': 0.13135930047694755, 'waiting_time': 0.7869093363167611}),
             (-0.0078125,
              {'y': 0.6131699135839903, 'waiting_time': 0.3819529093372579}),
             (0.0625,
              {'y': 0.0874609984399376, 'waiting_time': 0.37933841248842726}),
             (0.015625,
              {'y': 0.30620388762769585, 'waiting_time': 0.21827392315070515}),
             (0.03125,
              {'y': 0.12413824383164006, 'waiting_time': 0.8726993354999718}),
             (0.0078125,
              {'y': 0.6287949135839903, 'waiting_time': 0.8922832367658493}),
             (0.00390625,
              {'y': 0.8715190438995976, 'waiting_time': 0.10021472775766826}),
             (-0.00390625,
              {'y': 0.8637065438995976, 'waiting_time': 0.8853319158147196}),
             (-0.625,
              {'y': -0.6247440655192271, 'waiting_time': 0.8080330454118552}),
             (-0.875,
              {'y': -0.8748694048124327, 'waiting_time': 0.8081352670804669}),
             (0.875,
              {'y': 0.8751305951875673, 'waiting_time': 0.8545833891537951}),
             (0.625,
              {'y': 0.6252559344807729, 'waiting_time': 0.9780369027449156}),
             (0.375,
              {'y': 0.3757106057914372, 'waiting_time': 0.34587421366939364}),
             (0.01171875,
              {'y': 0.43307457758975415, 'waiting_time': 0.03574227852687384}),
             (-0.005859375,
              {'y': 0.7385633611533918, 'waiting_time': 0.05426611980614893}),
             (-0.01171875,
              {'y': 0.40963707758975415, 'waiting_time': 0.31105458756982274}),
             (0.005859375,
              {'y': 0.7502821111533918, 'waiting_time': 0.39007130571273296}),
             (-0.0234375,
              {'y': 0.1305706215220334, 'waiting_time': 0.32324455160039844}),
             (-0.009765625,
              {'y': 0.5020904154886127, 'waiting_time': 0.21611450960452627})])

Tutorial DataSaver#

Tutorial `DataSaver`#